Model Predictive Control (MPC) is the most widely used advanced process control technology in process industries, with more than 5,000 worldwide applications currently in service. MPC, which is sometimes also referred to as Multivariable Control (MVC), employs a model predictive controller that relies on dynamic models of an underlying process, e.g. linear models obtained by system identification.
A common and challenging problem is that MPC control performance will degrade with time due to inevitable changes in the underlying subject process, such as equipment modifications, changes in operating strategy, feed rate and quality changes, de-bottlenecking, instrumentation degradation, etc. Such degradation of control performance results in loss of benefits. Among all possible causes of control performance degradation, the model's predictive quality is the primary factor in most cases. To sustain good control performance, the model's predictive quality needs be monitored, and the model needs be periodically audited and updated if necessary.
However, it is a technically challenging and resource-intensive task to pinpoint a problematic model and re-identify a new model for replacement in a MPC application. In a large scale MPC application, over a hundred variables may be involved. Conducting a re-test and re-identification may take an experienced engineer weeks of intensive work and cause significant interruption to the normal operation as well.
Practically, only a subset of the model is to blame for control performance degradation in many applications. Techniques for isolating and then updating this Subset of the Model are sought (we will use “submodels” hereafter as the abbreviation for “subset of a model”).